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    This study introduces hierarchical controller-estimator algorithms (HCEAs) for networked Euler-Lagrange systems (NELSs), effectively managing sampled-data interactions and topology switches. These algorithms ensure system stability and are versatile for robotic systems.

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    Area of Science:

    • Control Systems Engineering
    • Robotics
    • Networked Systems

    Background:

    • Networked Euler-Lagrange systems (NELSs) present coordination challenges due to sampled-data interactions and dynamic topologies.
    • Existing methods often struggle to unify discontinuous and continuous signal cases.

    Purpose of the Study:

    • To develop novel hierarchical controller-estimator algorithms (HCEAs) for NELSs.
    • To address coordination problems under sampled-data interactions and switching topologies.
    • To unify the treatment of discontinuous and continuous signals within a single framework.

    Main Methods:

    • Proposed HCEAs with control, estimator, and optional filter layers.
    • Development of sufficient conditions for stabilizing cascade closed-loop systems.
    • Extension of algorithms to task-space coordination for heterogeneous robotic systems.

    Main Results:

    • Successful stabilization of NELSs with aperiodic sampling and control parameter constraints.
    • Demonstrated effectiveness in handling both discontinuous and continuous signals.
    • Validation of HCEA versatility for networked heterogeneous robotic systems.

    Conclusions:

    • The presented HCEAs offer a unified and effective framework for NELS coordination.
    • The algorithms provide robust solutions for sampled-data interactions and switching topologies.
    • The study highlights the significance and advantages of the developed algorithms through simulations.